Test data generation and scale up for database testing using unique common factor sequencing

ABSTRACT

Embodiments of the present invention provide a method for test data generation using unique common factor sequencing. In an embodiment of the invention, a method for test data generation using unique common factor sequencing is provided. The method includes loading a table for population with test data in a test data generation tool executing in a memory of a computer. A column set of multiple columns in the table associated with a key to the table is selected for processing and different cardinality sequence values are assigned to the columns in the set such that the cardinality sequence values do not share a common factor except for unity as in the case of prime numbers.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. application Ser. No.13/411,574, filed Mar. 4, 2012, now U.S. Pat. No. 8,549,046, which is aDivisional of U.S. application Ser. No. 12/982,742, filed Dec. 30, 2010,now U.S. Pat. No. 8,549,045, the entirety of which are incorporatedherein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to test data generation for large datasets and more particularly to test generation of data for databasetesting.

2. Description of the Related Art

A necessary step in the introduction of any new technology in a databasesystem is to test its behavior across a wide range of operatingconditions. This often involves selecting a set of test databases,generating representative query workloads, and executing these workloadson the test databases to evaluate the effect of the new technology. Theimportance of testing and benchmarking has long been recognized in thedatabase community and there are several standard benchmarks developedfor various settings.

While these standard benchmarks serve as useful reference points, thereis often a need to generate test databases that satisfy certainproperties on (for instance) table size, column domains, skew on columnsand correlation between columns. To this end database developerstraditionally generate synthetic data that satisfies required propertiesto adequately test the integrity and functionality of a database.

Of note, modern information systems work with extra large data sets.Thus, despite the sophistication and expected integrity of a databaseapplication and the quality of a set of test data created by a developerto test a database application, the proper operation of the databaseapplication cannot be assured under real life circumstances. To approachsimulation of real life circumstances, testing with an extra large dataset is an element of best practices management in testing a databaseprior to deployment. Yet, access to a reliably large enough data set foruse in testing all facets of a database application is not the norm.Rather, customarily, the data for the large data set must be generatedin an automated fashion.

Test data generators perform just this function. Generally, a test datagenerator can be viewed as a utility that generates at the minimum, rawdata, and for more sophisticated implementations, raw data, tables,views, and procedures for database testing purposes, performancetesting, quality assurance testing, loading tests or usability testing.Integral to the generation of any test data set, however, is thecreation of a fact table and a number of dimension tables. As it is wellknown, a fact table in the field of data warehousing consists of themeasurements, metrics or facts of a business process. The fact table isoften located at the centre of a star schema or a snowflake schema,surrounded by dimension tables and provide the additive values that actas independent variables by which dimensional attributes are analyzed.Dimension tables, in turn, contain attributes or fields used toconstrain and group data when performing data warehousing queries.

In generating data for the different columns of a fact table, randomdata is selected according to a sequence. In this regard, because thecolumn or columns of the fact table forming a primary key into the facttable must be unique, the sequence used in auto-populating the recordfields of those columns must avoid duplication through a cardinality ofsequence (the number of values in a sequence before the sequence repeatssuch as a cardinality of three for the sequence A, B, C, A, B, C, A, B,C or the cardinality of two for the sequence X, Y, X, Y, X, Y) that istoo small. The same problem exists for the column or columns of the facttable forming a foreign key into a dimension table. Also, the sameproblem exists for the column or columns of the fact table used in atable join with other tables.

BRIEF SUMMARY OF THE INVENTION

Embodiments of the present invention address deficiencies of the art inrespect to the generation of large data sets for database testing andprovide a novel and non-obvious method for test data generation usingunique common factor sequencing. In an embodiment of the invention, amethod for test data generation using unique common factor sequencing isprovided. The method includes loading a table for population with testdata in a test data generation tool executing in a memory of a computer.A column set of multiple columns in the table associated with a key tothe table can be selected for processing and different cardinalitysequence values are assigned to the columns in the set such that thecardinality sequence values do not share a common factor except forunity as in the case of prime numbers.

Thereafter, data is generated for the specified number of rows of eachcolumn in the column set according to a corresponding one of thecardinality sequence values and random data is additionally generatedfor other columns of the table without regard to any particularcardinality sequence value. Finally, the table can be persisted for usein database testing. Of note, in one aspect of the embodiment, themethod can include scaling up the table to a new table of an originalupper portion and an added lower portion by continuing into the addedlower portion a sequence for each column in the set of columns basedupon corresponding ones of the cardinality sequence values, whileduplicating data in the other columns of the data in the added lowerportion.

Additional aspects of the invention will be set forth in part in thedescription which follows, and in part will be obvious from thedescription, or may be learned by practice of the invention. The aspectsof the invention will be realized and attained by means of the elementsand combinations particularly pointed out in the appended claims. It isto be understood that both the foregoing general description and thefollowing detailed description are exemplary and explanatory only andare not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute partof this specification, illustrate embodiments of the invention andtogether with the description, serve to explain the principles of theinvention. The embodiments illustrated herein are presently preferred,it being understood, however, that the invention is not limited to theprecise arrangements and instrumentalities shown, wherein:

FIG. 1 is a pictorial illustration of a process for test data generationusing unique common factor sequencing;

FIG. 2 is a schematic illustration of a test data generation dataprocessing system configured for test data generation using uniquecommon factor sequencing; and,

FIG. 3A is a flow chart illustrating a process for test data generationusing unique common factor sequencing.

FIG. 3B is a flow chart illustrating a process for scaling up a tablegenerated using unique common factor sequencing.

DETAILED DESCRIPTION OF THE INVENTION

Embodiments of the invention provide for test data generation usingunique common factor sequencing. In accordance with an embodiment of theinvention, a set of columns for a table of test data each can beassigned a different cardinality of sequence for generating a sequenceof data that repeats according to a respective cardinality. Thecardinality of sequence for each column can be selected not to include acommon factor other than unity amongst the columns. Thereafter, data canbe generated for the set of columns according to the cardinality ofsequence for each column. Data can be generated randomly for othercolumns to the table without regard to any particular cardinality ofsequence. Additionally, the resultant fact table can be scaled up bycontinuing the sequence for the set of columns while simply duplicatingthe data in the other columns of the data. In this way, the fact tablecan be created quickly without risking duplicate keys or joins.

In further illustration, FIG. 1 is a pictorial illustration of a processfor test data generation using unique common factor sequencing. As shownin FIG. 1, a table 110 such as a fact table or base data set containingfull dimension tables (the full cartesian product of the key columns ofthe table) can be defined for population with automatically generatedtest data for testing a database 130. One or more key column sets 160 ofone or more key columns can be specified in the table 110 (only a singlekey column set shown in FIG. 1 for the purpose of illustrativesimplicity). Of import, cardinality sequence values 120 can be specifiedfor each column of the set 160. However, the cardinality sequence values120 for each individual column of the set 160 cannot share a commonfactor other than unity. In this regard, in one aspect of the embodimentillustrated in FIG. 1, the cardinality sequence values 120 can bedifferent prime numbers “P”, for instance 5, 3, and 2.

Thereafter, key column data generator 140 can generate a differentsequence of data for each column in the set 160 to repeat after thecardinality sequence value for a specified number of rows of the table110. The key column data generator 140 further can generate a differentsequence of data for each column in different sets (not shown) of keycolumns or join columns—that is columns used to refer to a record inanother table for combination with the records of the table 110. In thecase of a join column set, the columns of other tables referenced by thejoin columns are assigned the same cardinality sequence value 120 asthat of the corresponding join columns. In any event, the data for eachcolumn in a key column or join column can be computed according to theformula currentRowValue=((row_number−1) % cardinality_value)+1.Subsequently, the remaining columns of the table 110 can be populatedwith random data. Optionally, scale up processor 150 can augment thetable 110 by duplicating the random data while expanding the sequencesof the key columns and join columns so as to ensure unique combinationsof each row of the key column and join column sets.

The process described in connection with FIG. 1 can be implemented in atest data generation data processing system. In further illustration,FIG. 2 schematically shows a test data generation data processing systemconfigured for test data generation using unique common factorsequencing. As shown in FIG. 2, the system can include a host computer210 with at least one processor and memory. An operating system 230 canexecute in the memory of the host computer 210 and can support theoperation of a test data generator 240 configured to generate test datain a base data table 250 such as a fact table of a database 220.Finally, the system can include a unique common factor sequencing module300 coupled to the test data generator 240.

The unique common factor sequencing module 300 can include program codethat when executed in the memory of the host computer 210 can be enabledto select one or more columns of the table 250 as a key column set orjoin column set. The program code of the unique common factor sequencingmodule 300 further can be enabled to establish a different cardinalitysequence value for each column in the set subject to the constraint thateach different cardinality sequence value cannot share a common factorwith another cardinality sequence value for another column in the set.To ensure that each different cardinality sequence value does not sharea common factor with another cardinality sequence value for anothercolumn in the set, each different cardinality sequence value can beestablished as a prime number.

The program code of the unique common factor sequencing module 300further can be enabled upon execution in the memory of the host computer210 to generate sequences of data for each column of the key column setaccording to a corresponding established cardinality sequence value. Inthis regard, the column data generation formula of unique common factorsequencing module 300 can be applied to populate the row values of thekey column set. For instance, a key column set for three columns withrespective cardinality sequence values of 5, 3 and 2 can produce thefive by five fact table embedded herein:

ROW COLUMN COLUMN COLUMN COLUMN COLUMN NUM 1 2 3 4 5 1 1 1 1 65 23 2 2 22 56 53 3 3 3 1 67 59 4 4 1 2 76 79 5 5 2 1 87 98

In yet further illustration of the operation of the unique common factorsequencing module 300, FIG. 3A is a flow chart illustrating a processfor test data generation using unique common factor sequencing.Beginning in block 305, a table structure of a specified number ofcolumns and rows can be defined for a table of base data for a database,such as a fact table. In block 310, one or more columns of the table canbe specified to form one or more key column set or join column sets. Inblock 315, a first column set can be selected for processing. In block320, a set of cardinality sequence values can be established for thecolumn or columns of the column set. In decision block 325, it can bedetermined whether or not the established cardinality sequence values donot share a common factor other than unity. If it is determined that theestablished cardinality sequence values share a common factor other thanunity, in block 330 an error message can be displayed and again in block320, a set of cardinality sequence values can be established for thecolumn or columns of the column set.

In decision block 325, if it is determined that the establishedcardinality sequence values do not share a common factor other thanunity, in block 335, the row values for each column in the column setcan be populated with different values resulting from a sequence ofvalue computed according to the formula:currentRowValue=((row_number−1)% cardinality_value)+1In decision block 340, if additional column sets for keys or joinsremain to be processed, in block 345 a next column set can be retrievedfor processing and the process can repeat through block 320. Otherwise,in block 350 random data can be generated for the remaining columns ofthe table.

In decision block 355, it can be determined whether or not to scale upthe table produced through the operation of blocks 335 and 350. If not,the table as generated can be persisted for database testing in block365. Otherwise, in block 360, scale up processing can be performed onthe table. In more particular illustration, FIG. 3B is a flow chartillustrating a process for scaling up a table generated using uniquecommon factor sequencing. Beginning in block 370, a row count and columncount can be determined for the table. In block 375, a new table can begenerated to include additionally rows and columns. In block 380, one ormore columns of the table can be specified to form one or more keycolumn set or join column sets that correspond to the column sets of theoriginal table. In block 385, the data of the original table can becopied into an upper portion of the new table corresponding to theoriginal table and also a new, lower portion of the new table.

However, in block 390, the column sets of the lower portion of the newtable can be updated to continue the sequencing according to themodified cardinality sequence values assigned to each column in thecolumn sets. In this regard, the values of the rows of the columns inthe column set in the lower portion of the new table can be computedaccording to the formula:modifiedRowValue=(L+V−1%R)+1where L is the last value in the original table for the column, V is theexisting value for the row of the column and R is the range for thecolumn. Of note, a modified form of the foregoing formula for scaling upa fact table can be applied to scale up dimension tables as follows:modifiedRowValue=(L+V−1%R)+1+(M−R)where L is the last value in the original table for the column, V is theexisting value for the row of the column, M is the maximum value for thecolumn in the dimension table and R is the range for the column.

Of note, the scaling up process described above can be augmented toprovide scalability when addressing hash distributed tables. In thisregard, to ensure the integrity of the foregoing process when performedin isolation on different servers in parallel for hash partitionedtables, no hash distribution key columns are scaled up. Instead, hashdistribution key columns are duplicated during the scaling up process.In the instance where there is only a single column hash distributionkey, the number of rows inserted into the upper and lower portions ofthe new table must be either a multiple of the range of the column, or aprime number less than the range of the column. In the instance wherethere is a composite hash distribution key of multiple columns, a rangeis calculated for the distribution key as a whole to be the largestprime number less than the product of the ranges of all of the columnsof the composite hash distribution key. Subsequently, the number of rowscreated for the upper and lower portions of the new table must be eithera multiple of the range, or a prime number less than the range.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, radiofrequency, and the like, or anysuitable combination of the foregoing. Computer program code forcarrying out operations for aspects of the present invention may bewritten in any combination of one or more programming languages,including an object oriented programming language and conventionalprocedural programming languages. The program code may execute entirelyon the user's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention have been described above withreference to flowchart illustrations and/or block diagrams of methods,apparatus (systems) and computer program products according toembodiments of the invention. In this regard, the flowchart and blockdiagrams in the Figures illustrate the architecture, functionality, andoperation of possible implementations of systems, methods and computerprogram products according to various embodiments of the presentinvention. For instance, each block in the flowchart or block diagramsmay represent a module, segment, or portion of code, which comprises oneor more executable instructions for implementing the specified logicalfunction(s). It should also be noted that, in some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts, or combinations of special purpose hardware andcomputer instructions.

It also will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks. The computer program instructions may also beloaded onto a computer, other programmable data processing apparatus, orother devices to cause a series of operational steps to be performed onthe computer, other programmable apparatus or other devices to produce acomputer implemented process such that the instructions which execute onthe computer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

Finally, the terminology used herein is for the purpose of describingparticular embodiments only and is not intended to be limiting of theinvention. As used herein, the singular forms “a”, “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will be further understood that the terms“comprises” and/or “comprising,” when used in this specification,specify the presence of stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

Having thus described the invention of the present application in detailand by reference to embodiments thereof, it will be apparent thatmodifications and variations are possible without departing from thescope of the invention defined in the appended claims as follows.

We claim:
 1. A method for test data generation using unique commonfactor sequencing, the method comprising: loading a first table forpopulation with test data in a test data generation tool executing in amemory of a computer; selecting a column set of multiple columns in thefirst table associated with a key to the first table; assigningdifferent cardinality sequence values to each column in the column setof multiple columns, wherein the cardinality sequence values do notshare a common factor except for unity and each cardinality sequencevalue indicates a number of values in a sequence before the sequencerepeats; generating data for a specified number of rows of each columnin the column set of multiple columns according to a corresponding oneof the cardinality sequence values; additionally generating random datafor other columns of the first table without regard to any particularcardinality sequence value; persisting the first table for use indatabase testing; and scaling up the first table to a new table of anoriginal upper portion and an added lower portion by continuing into theadded lower portion of the new table a sequence for each column in thecolumn set of multiple columns based upon corresponding ones of thecardinality sequence values and based upon computing values of rows foreach column in the column set of multiple columns in the added lowerportion of the new table according to a formula:modifiedRowValue=(L+V−1% R)+1, where L is a last value in the firsttable for the column, V is an existing value for a row of the column inthe new table, and R is a range for the column in the new table, whileduplicating data in the other columns of the first table in the addedlower portion of the new table.
 2. The method of claim 1, wherein thecardinality sequence values are different prime numbers.
 3. The methodof claim 1, further comprising: identifying at least one column of thefirst table corresponding to a hash distribution key; and duplicatingdata from all columns of the first table that corresponds to the hashdistribution key into the new table, but creating both the originalupper portion and the added lower portion of the new table, each with anumber of new rows as a prime number less than or equal to a product ofranges of the columns corresponding to the hash distribution key.
 4. Amethod for test data generation using unique common factor sequencing,the method comprising: loading a first table for population with testdata in a test data generation tool executing in a memory of a computer;selecting a column set of multiple columns in the first table associatedwith a key to the first table; assigning different cardinality sequencevalues to each column in the column set of multiple columns, wherein thecardinality sequence values do not share a common factor except forunity and each cardinality sequence value indicates a number of valuesin a sequence before the sequence repeats; generating data for aspecified number of rows of each column in the column set of multiplecolumns according to a corresponding one of the cardinality sequencevalues; additionally generating random data for other columns of thefirst table without regard to any particular cardinality sequence value;persisting the first table for use in database testing; and scaling upthe first table to a new table of an original upper portion and an addedlower portion by continuing into the added lower portion of the newtable a sequence for each column in the column set of multiple columnsbased upon corresponding ones of the cardinality sequence values andbased upon computing values of rows for each column in the column set ofmultiple columns in the added lower portion of the new table accordingto a formula:modifiedRowValue=(L+V−1% R)+1+(M−R), where L is a last value in thefirst table for the column, V is an existing value for a row of thecolumn in the new table, M is a maximum value for the column in the newtable, and R is a range for the column in the new table, whileduplicating data in the other columns of the first table in the addedlower portion of the new table.
 5. The method of claim 4, wherein thecardinality sequence values are different prime numbers.
 6. The methodof claim 4, further comprising: identifying at least one column of thefirst table corresponding to a hash distribution key; and duplicatingdata from all columns of the first table that corresponds to the hashdistribution key into the new table, but creating both the originalupper portion and the added lower portion of the new table, each with anumber of new rows as a prime number less than or equal to a product ofranges of the columns corresponding to the hash distribution key.